Evaluation of NTCM-BC and a proposed modification for single-frequency positioning
- 594 Downloads
Ionospheric delay is a dominant factor that affects the accuracy of single-frequency positioning. Thus, an empirical ionospheric model with high accuracy is very important for single-frequency users. This study proposes a modified empirical broadcast ionospheric model, called MNTCM-BC, based on the Neustrelitz Total Electron Content (TEC) broadcast model NTCM-BC. Nine daily ionospheric coefficients of these models are estimated using datasets of the previous day from 30 globally distributed Global Navigation Satellite System monitor stations, and the prediction performance of the MNTCM-BC is evaluated with the datasets of the current day from all 300 verification stations. The results show that the complex behavior of the ionosphere is well described by the MNTCM-BC, including the visibility of two ionization crests on both sides of the geomagnetic equator and the TEC variations that depend on the local time and geomagnetic latitude. In terms of the prediction accuracy, compared with the NTCM-BC, the main improvement in the MNTCM-BC is achieved in summer, whereas the accuracy is comparable in other seasons. Hence, the following analyses are focused on summer. In the low-solar activity year of 2009, the prediction accuracy of the MNTCM-BC is improved by 0.11 TECU compared with that of the NTCM-BC. As to the high-solar activity year of 2014, the corresponding improvement is 0.35 TECU. In addition, when the number of monitor stations is increased from 30 to 300, the prediction accuracy of two models can be slightly improved by 0.06 TECU in 2009 and 0.13 TECU in 2014, respectively, while reliability enhances. Furthermore, the average three-dimensional positioning accuracy of 160 globally distributed stations for single-frequency point positioning using the Klobuchar model, the NTCM-BC and the MNTCM-BC is 1.83, 1.21 and 1.20 m during quiet day and 3.15, 2.31 and 2.21 m during perturbed day, respectively. Relative to the Klobuchar model and the NTCM-BC, the average accuracy improvements in the MNTCM-BC are about 30 and 3%, respectively.
KeywordsIonospheric delay GNSS Broadcast ionospheric model Ionization crests Single-frequency positioning Modified Neustrelitz TEC broadcast model (MNTCM-BC)
We would like to express our gratitude to reviewers for their helpful remarks. Additionally, we acknowledge the IGS, SIDC and ISGI for providing access to GNSS data and products, sunspot number and geomagnetic indices, respectively. This study is supported by National Natural Science Foundation of China (Grant No. 41474025), the Surveying and Mapping Foundation Research Fund Program, National Administration of Surveying, Mapping and Geoinformation (Grant No. 14-02-09) and the Open Foundation of Key Laboratory of Precise Engineering and Industry Surveying of the National Administration of Surveying, Mapping and Geoinformation (Grant No. PF2015-5).
- Bent RB, Llewllyn SK (1973) Documentation and description of the Bent ionospheric model. SAMSO technical report, pp 73–252Google Scholar
- Bidaine B (2012) Ionosphere modelling for Galileo single frequency users. Ph.D thesis. University of Liège, Liège, BelgiumGoogle Scholar
- Hoque MM, Jakowski N (2011) A new global empirical NmF2 model for operational use in radio systems. Radio Sci 46:191–200Google Scholar
- Hoque MM, Jakowski N, Jens B (2015b) An ionosphere broadcast model for next generation GNSS. ION GNSS 2015, Institute of Navigation, Tampa, Florida, USA, 14–18 September, pp 3755–3765Google Scholar
- Jakowski N, Hoque MM (2012) Ionospheric range error correction models. In: Proc of international conference on localization and GNSS (ICL-GNSS), Starnberg, Germany, 25–27 June, IEEE Xplore. doi: 10.1109/ICL-GNSS.2012.6253110
- Klobuchar JA (1975) A first-order, worldwide, ionospheric, time-delay algorithm. United States Air Force, Air Force Systems Command, Air Force Cambridge Research Laboratories, Ionospheric Physics Laboratory, BedfordGoogle Scholar
- Liu L, Chen Y (2009) Statistical analysis of solar activity variations of total electron content derived at jet propulsion laboratory from GPS observations. J Geophys Res Atmos 114(A10):125–134Google Scholar
- Ren X, Zhang X, Xie W, Zhang K, Yuan Y, Li X (2016) Global Ionospheric Modelling using Multi-GNSS: BeiDou, Galileo, GLONASS and GPS. Scientific Reports 6, 33499; doi: 10.1038/srep33499
- Schaer S (1999) Mapping and predicting the Earth’s ionosphere using the Global Positioning System. Ph.D. thesis. AIUB, University of BerneGoogle Scholar
- Seber GAF, Wild CJ (1989) Unconstrained optimization. In:Nonlinear regression. Wiley, New Jersey, ISBN:0-471-47135-6Google Scholar